GalaxyCloudRunner: enhancing scalable computing for Galaxy

Nuwan Goonasekera; Alexandru Mahmoud; John Chilton; Enis Afgan

doi:10.1093/bioinformatics/btaa860

GalaxyCloudRunner: enhancing scalable computing for Galaxy

Bioinformatics. 2021 Jul 19;37(12):1763-1765. doi: 10.1093/bioinformatics/btaa860.

Authors

Nuwan Goonasekera¹, Alexandru Mahmoud², John Chilton³, Enis Afgan²

Affiliations

¹ Melbourne Bioinformatics, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC 3010, Australia.
² Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA.
³ Department of Biochemistry and Molecular Biology, Penn State University, State College, PA 16801, USA.

Abstract

Summary: The existence of more than 100 public Galaxy servers with service quotas is indicative of the need for an increased availability of compute resources for Galaxy to use. The GalaxyCloudRunner enables a Galaxy server to easily expand its available compute capacity by sending user jobs to cloud resources. User jobs are routed to the acquired resources based on a set of configurable rules and the resources can be dynamically acquired from any of four popular cloud providers (AWS, Azure, GCP or OpenStack) in an automated fashion.

Availability and implementation: GalaxyCloudRunner is implemented in Python and leverages Docker containers. The source code is MIT licensed and available at https://github.com/cloudve/galaxycloudrunner. The documentation is available at http://gcr.cloudve.org/.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Azure Stains
Computational Biology*
Documentation
Humans
Software*

Substances

Azure Stains

Abstract

Publication types

MeSH terms

Substances

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